Eyelid Localization for Iris Identification

This article presents a new eyelid localization algorithm based on a parabolic curve fitting. To deal with eyelashes, low contrast or false detection due to iris texture, we propose a two steps algorithm. First, possible edge candidates are selected by applying edge detection on a restricted area inside the iris. Then, a gradient maximization is applied along every parabola, on a larger area, to refine parameters and select the best one. Experiments have been conducted on a database of 151 iris that have been manually segmented. The performance evaluation is carried out by comparing the segmented images obtained by the proposed method with the manual segmentation. The results are satisfactory in more than 90% of the cases

Multispectral Palmprint Recognition Using a Quaternion Matrix

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%

Оценка качества и идентификация отпечатков пальцев путем анализа структурных свойств изображения

The paper addresses the problem of assessing the quality of fingerprint images using spatial analysis methods. The author proposes using the previously developed mathematical model to describe the set of magnitudes of the image gradient. The model is based on the two-parameter Weibull distribution. The author proposes two approaches to assess the quality of fingerprints. The first approach is implemented by the so-called “Full reference method”, which compares the Weibull distribution parameters’ values of statistical estimates. The results of identifying sweat pores using this method are presented. The second approach is called the “No-Reference method” and is used to assess fingerprints’ quality when analyzing and identifying the information content of their individual sections. It is proposed to use an image blur map as a quality characteristic and a statistical estimate of the Weibull distribution shape parameter as a measure of the blur. The shape parameter is estimated at each image point by the combination of magnitudes of the image gradient in the vicinity of the point; in this, the previously developed blur mapping technique is applied. The specific examples illustrate effectiveness of the proposed approaches.Рассмотрена задача оценки качества изображения отпечатков пальцев с применением пространственных методов анализа. Предложено использовать математическую модель, разработанную ранее для описания совокупности магнитуд градиента изображения. Модель основана на двухпараметрическом распределении Вейбулла. Для оценки качества отпечатков пальцев предложены два подхода. Первый реализуется с помощью так называемого метода сравнения с эталоном (Full Reference), когда сравниваются значения статистических оценок параметров распределения Вейбулла. Приведены результаты решения задачи идентификации потовых пор этим методом. Второй подход называется «безэталонным» (No-Reference) и применяется для оценки качества отпечатков при анализе и выделении информативности их отдельных участков. В качестве характеристики качества предлагается использовать карту размытости изображения, а в качестве меры размытости – статистическую оценку параметра формы распределения Вейбулла. Параметр формы оценивается в каждой точке изображения по совокупности магнитуд градиента изображения в окрестности точки, при этом применяется разработанная ранее методика построения карты размытости. Эффективность предложенных подходов иллюстрируется конкретными примерами

On the architecture of H.264 to H.264 homogeneous transcoding platform

2007-2008 > Academic research: refereed > Invited conference paperVersion of RecordPublishe

Reversible Deep Neural Network Watermarking:Matching the Floating-point Weights

Static deep neural network (DNN) watermarking embeds watermarks into the weights of DNN model by irreversible methods, but this will cause permanent damage to watermarked model and can not meet the requirements of integrity authentication. For these reasons, reversible data hiding (RDH) seems more attractive for the copyright protection of DNNs. This paper proposes a novel RDH-based static DNN watermarking method by improving the non-reversible quantization index modulation (QIM). Targeting the floating-point weights of DNNs, the idea of our RDH method is to add a scaled quantization error back to the cover object. Two schemes are designed to realize the integrity protection and legitimate authentication of DNNs. Simulation results on training loss and classification accuracy justify the superior feasibility, effectiveness and adaptability of the proposed method over histogram shifting (HS).Comment: 21 page

Image zooming based on sampling theorems

In this paper we introduce two digital zoom methods based on sampling theory and we study their mathematical foundation. The first one (usually known by the names of "sinc interpolation", "zero-padding" and "Fourier zoom") is commonly used by the image processing community